基于粒子群优化神经网络的居民天然气用户漏气检测

IF 3.1 4区工程技术 Q3 ENERGY & FUELS

Energy Sources Part B-Economics Planning and Policy Pub Date : 2022-12-15 DOI:10.1080/15567249.2022.2154412

A. Soltanisarvestani, A. Safavi, M. Rahimi

{"title":"基于粒子群优化神经网络的居民天然气用户漏气检测","authors":"A. Soltanisarvestani, A. Safavi, M. Rahimi","doi":"10.1080/15567249.2022.2154412","DOIUrl":null,"url":null,"abstract":"ABSTRACT One of the most important issues related to natural gas is unaccounted for gas. Residential customers constitute a significant percentage of unaccounted for gas. To estimate the amount of unaccounted for gas, it is necessary to compare the amount of consumption estimated by the model with the one recorded by the meter. Thus, the value estimated by the consumption model are of great importance. Initially, a consumption model is developed for each customer using consumption data for the first 12 months and the average monthly ambient outdoor temperature related to the same time period. The models are developed using artificial neural networks and particle swarm optimization algorithm. The estimates made by the models are then compared with the values recorded by the meters. This method is then implemented on some real data (as the study area). The results show the effectiveness of the proposed method.","PeriodicalId":51247,"journal":{"name":"Energy Sources Part B-Economics Planning and Policy","volume":"123 1","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Detection of Unaccounted for Gas in Residential Natural Gas Customers Using Particle Swarm Optimization-based Neural Networks\",\"authors\":\"A. Soltanisarvestani, A. Safavi, M. Rahimi\",\"doi\":\"10.1080/15567249.2022.2154412\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT One of the most important issues related to natural gas is unaccounted for gas. Residential customers constitute a significant percentage of unaccounted for gas. To estimate the amount of unaccounted for gas, it is necessary to compare the amount of consumption estimated by the model with the one recorded by the meter. Thus, the value estimated by the consumption model are of great importance. Initially, a consumption model is developed for each customer using consumption data for the first 12 months and the average monthly ambient outdoor temperature related to the same time period. The models are developed using artificial neural networks and particle swarm optimization algorithm. The estimates made by the models are then compared with the values recorded by the meters. This method is then implemented on some real data (as the study area). The results show the effectiveness of the proposed method.\",\"PeriodicalId\":51247,\"journal\":{\"name\":\"Energy Sources Part B-Economics Planning and Policy\",\"volume\":\"123 1\",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2022-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy Sources Part B-Economics Planning and Policy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/15567249.2022.2154412\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy Sources Part B-Economics Planning and Policy","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/15567249.2022.2154412","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}

引用次数: 0

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

The Detection of Unaccounted for Gas in Residential Natural Gas Customers Using Particle Swarm Optimization-based Neural Networks

ABSTRACT One of the most important issues related to natural gas is unaccounted for gas. Residential customers constitute a significant percentage of unaccounted for gas. To estimate the amount of unaccounted for gas, it is necessary to compare the amount of consumption estimated by the model with the one recorded by the meter. Thus, the value estimated by the consumption model are of great importance. Initially, a consumption model is developed for each customer using consumption data for the first 12 months and the average monthly ambient outdoor temperature related to the same time period. The models are developed using artificial neural networks and particle swarm optimization algorithm. The estimates made by the models are then compared with the values recorded by the meters. This method is then implemented on some real data (as the study area). The results show the effectiveness of the proposed method.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Energy Sources Part B-Economics Planning and Policy ENERGY & FUELS-

CiteScore

6.80

自引率

12.80%

发文量

审稿时长

6-12 weeks

期刊介绍： 12 issues per year Abstracted and/or indexed in: Applied Science & Technology Index; API Abstracts/Literature; Automatic Subject Index Citation; BIOSIS Previews; Cabell’s Directory of Publishing Opportunities in Economics and Finance; Chemical Abstracts; CSA Aquatic Science & Fisheries Abstracts; CSA Environmental Sciences & Pollution Management Database; CSA Pollution Abstracts; Current Contents/Engineering, Technology & Applied Sciences; Directory of Industry Data Sources; Economic Abstracts; Electrical and Electronics Abstracts; Energy Information Abstracts; Energy Research Abstracts; Engineering Index Monthly; Environmental Abstracts; Environmental Periodicals Bibliography (EPB); International Abstracts in Operations Research; Operations/Research/Management Science Abstracts; Petroleum Abstracts; Physikalische Berichte; and Science Citation Index. Taylor & Francis make every effort to ensure the accuracy of all the information (the "Content") contained in our publications. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor & Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to, or arising out of the use of the Content. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions .